Simulation of the dynamic behavior of mitral valve closure could improve clinical treatment by predicting surgical procedures outcome. We propose here a method to achieve this goal by using the immersed boundary method. In order to go towards patient-based simulation, we tailor our method to be adapted to a valve extracted from medical image data. It includes investigating segmentation process, smoothness of geometry, case setup and the shape of the left ventricle. We also study the influence of leaflet tissue anisotropy on the quality of the valve closure by comparing with an isotropic model. As part of the anisotropy analysis, we study the influence of the principal material direction by comparing methods to obtain them without dissection. Results show that our method can be scaled to various image-based data. We evaluate the mitral valve closure quality based on measuring bulging area, contact map, and flow rate. The results show also that the anisotropic material model more precisely represents the physiological characteristics of the valve tissue. Furthermore, results indicate that the orientation of the principal material direction plays a role in the effectiveness of the valve seal.

The dynamic behavior of the mitral valve (MV) is highly influenced by the material model used to describe the leaflet motion. Due to the presence of collagen fibers, MV leaflets show an anisotropic behavior. The aim of this study is to investigate the influence of anisotropy on the fluid-structure interaction (FSI) simulation of the MV dynamic closure. The FSI simulation of the MV is performed using an immersed boundary method. Two constitutive models, Holzapfel-Gasser-Ogden for the anisotropic, and third-order Ogden for the isotropic are used. For the anisotropic model, two fiber directions, one that is parallel to the annulus surface and another that follows an arc on the leaflets are considered. In order to take into account the effects of both the chordae structure and leaflet geometry, generic and image-based MV are studied. The quality of the closure is evaluated based on measuring the bulging area, contact map, and flow rate. In both generic and image-based, a significant difference is observed between the anisotropic and isotropic cases. Additionally, the chordae forces during the closure are compared with ex-vivo data of the literature and show good similarities with these results.

Abstract. Purpose: In ultrasound imaging, interpolation is a key step in converting scanline data to brightness-mode (B-mode) images. Conventional methods, such as bilinear interpolation, do not fully capture the spatial dependence between data points, which leads to deviations from the underlying probability distribution at the interpolation points. Approach: We propose Gaussian process (GP) regression as an improved method for ultrasound scanline interpolation. Using ultrasound scanlines acquired from two different ultrasound scanners during in vivo trials, we compare the scanline conversion accuracy of three standard interpolation methods with that of GP regression, measuring the peak signal-to-noise ratio (PSNR) and mean absolute error (MAE) for each method. Results: The PSNR and MAE scores show that GP regression leads to more accurate scanline conversion compared to the nearest neighbor, bilinear, and cubic spline interpolation methods, for both datasets. Furthermore, limiting the interpolation window size of GP regression to 15 reduces computation time with minimal to no reduction in PSNR. Conclusions: GP regression quantitatively leads to more accurate scanline conversion and provides uncertainty estimates at each of the interpolation points. Our windowing method reduces the computational cost of using GP regression for scanline conversion.

The goal of this study was to develop an algorithm that enhances the temporal resolution of two-dimensional color Doppler echocardiography (2D CDE) by reordering all the acquired frames and filtering out the frames corrupted by out-of-plane motion and arrhythmia. The algorithm splits original frame sequence into the fragments based on the correlation with a reference frame. Then, the fragments are aligned temporally and merged into a resulting sequence that has higher temporal resolution. We evaluated the algorithm with 10 animal epicardial 2D CDE datasets of the right ventricle and compared it with the existing approaches in terms of resulting frame rate, image stability and execution time. We identified the optimal combination of alternatives for each step, which resulted in an increase in frame rate from 14 ± 0.87 to 238 ± 93 Hz. The average execution time was 7.23 ± 0.48 s in comparison with 0.009 ± 0.001 s for ECG gating and 1167.37 ± 587.85 s for flow reordering. Our approach demonstrated a significant (p < 0.01) increase in image stability compared with ECG gating and flow reordering. This work presents an offline algorithm for temporal enhancement of 2D CDE. Unlike previous frame reordering approaches, it can filter out-of-plane or corrupted frames, increasing the quality of the results, which substantially increases diagnostic value of 2D CDE. It can be used for high-frame-rate intraoperative imaging of intraventricular and valve regurgitant flows and is potentially modifiable for real-time use on ultrasound machines.

Purpose High dynamic range (HDR) imaging is a popular computational photography technique that has found its way into every modern smartphone and camera. In HDR imaging, images acquired at different exposures are combined to increase the luminance range of the final image, thereby extending the limited dynamic range of the camera. Ultrasound imaging suffers from limited dynamic range as well; at higher power levels, the hyperechogenic tissue is overexposed, whereas at lower power levels, hypoechogenic tissue details are not visible. In this work, we apply HDR techniques to ultrasound imaging, where we combine ultrasound images acquired at different power levels to improve the level of detail visible in the final image. Methods Ultrasound images of ex vivo and in vivo tissue are acquired at different acoustic power levels and then combined to generate HDR ultrasound (HDR-US) images. The performance of five tone mapping operators is quantitatively evaluated using a similarity metric to determine the most suitable mapping for HDR-US imaging. Results The ex vivo and in vivo results demonstrated that HDR-US imaging enables visualizing both hyper- and hypoechogenic tissue at once in a single image. The Durand tone mapping operator preserved the most amount of detail across the dynamic range. Conclusions Our results strongly suggest that HDR-US imaging can improve the utility of ultrasound in image-based diagnosis and procedure guidance.

BACKGROUND:Common surgical procedures on the mitral valve of the heart include modifications to the chordae tendineae. Such interventions are used when there is extensive leaflet prolapse caused by chordae rupture or elongation. Understanding the role of individual chordae tendineae before operating could be helpful to predict whether the mitral valve will be competent at peak systole. Biomechanical modelling and simulation can achieve this goal.METHODS:We present a method to semi-automatically build a computational model of a mitral valve from micro CT (computed tomography) scans: after manually picking chordae fiducial points, the leaflets are segmented and the boundary conditions as well as the loading conditions are automatically defined. Fast finite element method (FEM) simulation is carried out using Simulation Open Framework Architecture (SOFA) to reproduce leaflet closure at peak systole. We develop three metrics to evaluate simulation results: (i) point-to-surface error with the ground truth reference extracted from the CT image, (ii) coaptation surface area of the leaflets and (iii) an indication of whether the simulated closed leaflets leak.RESULTS:We validate our method on three explanted porcine hearts and show that our model predicts the closed valve surface with point-to-surface error of approximately 1 mm, a reasonable coaptation surface area, and absence of any leak at peak systole (maximum closed pressure). We also evaluate the sensitivity of our model to changes in various parameters (tissue elasticity, mesh accuracy, and the transformation matrix used for CT scan registration). We also measure the influence of the positions of the chordae tendineae on simulation results and show that marginal chordae have a greater influence on the final shape than intermediate chordae.CONCLUSIONS:The mitral valve simulation can help the surgeon understand valve behaviour and anticipate the outcome of a procedure.

Background: Assessment of left atrial (LA) size and function is important in a number of pediatric cardiac conditions including those affecting the diastolic performance of the left ventricle. Measurements of LA volume and strain by two-dimensional echocardiography rely upon inaccurate geometric assumptions and are hampered by out-of-plane motion. The objective of this study was to characterize LA volumes and strain by three-dimensional echocardiography in healthy children. Methods: LA volumes and strain were retrospectively measured by three-dimensional echocardiography in healthy children with no known structural or functional heart disease using a commercial speckle-tracking package applied to the LA to compute maximum volume (V-max), minimum volume (V-min), ejection volume (LAEV), ejection fraction (LAEF), and the following components of global strain: 3D principal (3DS), longitudinal (GLS), and circumferential (GCS). Results: The study population included 196 normal subjects (median age, 12 years; range, 4 days to 20.9 years). V-max, V-min, and LAEV increased with age and body surface area. Significant age-related declines were present in all measured functional indices including LAEF, 3DS, GLS, and GCS. Analysis of a subset of 50 subjects showed excellent agreement between V-max derived by three-dimensional and two-dimensional biplane area-length method. Regression equations with standard deviations were generated to enable calculation of Z scores. Conclusions: LA volume and functional indices can be reliably calculated using a commercial three-dimensional analysis software. All components of LA strain decline modestly with age. Normative data generated in this study have the potential to greatly enhance the utility of three-dimensional echocardiography-derived measurements in a wide range of cardiac pathologies.

Objective: Aortic valve repair is currently in transition from surgical improvisation to a reproducible operation and an option for many patients with aortic regurgitation. Our research efforts at improving reproducibility include development of methods for intraoperatively testing and visualizing the valve in its diastolic state. Methods: We developed a device that can be intraoperatively secured in the transected aorta allowing the aortic root to be pressurized and the closed valve to be inspected endoscopically. Our device includes a chamber that can be pressurized with crystalloid solution and ports for introduction of an endoscope and measuring gauges. We show use of the device in explanted porcine hearts to visualize the aortic valve and to measure leaflet coaptation height in normal valves and in valves that have undergone valve repair procedures. Results: The procedure of introducing and securing the device in the aorta, pressurizing the valve, and endoscopically visualizing the closed valve is done in less than 1 minute. The device easily and reversibly attaches to the aortic root and allows direct inspection of the aortic valve under conditions that mimic diastole. It enables the surgeon to intraoperatively study the valve immediately before repair to determine mechanisms of incompetence and immediately after the repair to assess competence. We also show its use in measuring valve leaflet coaptation height in the diastolic state. Conclusions: This device enables more relevant prerepair valve assessment and also enables a test of postrepair valve competence under physiological pressures.

In this paper we describe a pilot study, where machine learning methods are used to differentiate between congenital heart diseases. Our approach was to apply convolutional neural networks (CNNs) to echocardiographic images from five different pediatric populations: normal, coarctation of the aorta (CoA), hypoplastic left heart syndrome (HLHS), transposition of the great arteries (TGA), and single ventricle (SV). We used a single network topology that was trained in a pairwise fashion in order to evaluate the potential to differentiate between patient populations. In total we used 59,151 echo frames drawn from 1,666 clinical sequences. Approximately 80% of the data was used for training, and the remainder for validation. Data was split at sequence boundaries to avoid having related images in the training and validation sets. While training was done with echo images/frames, evaluation was performed for both single frame discrimination as well as sequence discrimination (by majority voting). In total 10 networks were generated and evaluated. Unlike other domains where this network topology has been used, in ultrasound there is low visual variation between classes. This work shows the potential for CNNs to be applied to this low-variation domain of medical imaging for disease discrimination.

Abstract. Coarctation of aorta (CoA) is a critical congenital heart defect (CCHD) that requires accurate and immediate diagnosis and treatment. Current newborn screening methods to detect CoA lack both in sensitivity and specificity, and when suspected in a newborn, it must be confirmed using specialized imaging and expert diagnosis, both of which are usually unavailable at tertiary birthing centers. We explore the feasibility of applying machine learning methods to reliably determine the presence of this difficult-to-diagnose cardiac abnormality from ultrasound image data. We propose a framework that uses deep learning-based machine learning methods for fully automated detection of CoA from two-dimensional ultrasound clinical data acquired in the parasternal long axis view, the apical four chamber view, and the suprasternal notch view. On a validation set consisting of 26 CoA and 64 normal patients our algorithm achieved a total error rate of 12.9% (11.5% false-negative error and 13.6% false-positive error) when combining decisions of classifiers over three standard echocardiographic view planes. This compares favorably with published results that combine clinical assessments with pulse oximetry to detect CoA (71% sensitivity).

Medical implants of fixed size cannot accommodate normal tissue growth in children and often require eventual replacement or—in some cases—removal, leading to repeated interventions, increased complication rates and worse outcomes. Implants that can correct anatomical deformities and accommodate tissue growth remain an unmet need. Here, we report the design and use of a growth-accommodating device for paediatric applications that consists of a biodegradable core and a tubular braided sleeve, with inversely related sleeve length and diameter. The biodegradable core constrains the diameter of the sleeve, and gradual core degradation following implantation enables sleeve and overall device elongation to accommodate tissue growth. By means of mathematical modelling and ex vivo experiments using harvested swine hearts, we demonstrate the predictability and tunability of the behaviour of the device for disease- and patient-specific needs. We also used the rat tibia and the piglet heart valve as two models of tissue growth to demonstrate that polymer degradation enables device expansion and growth accommodation in vivo.

Two-dimensional color Doppler echocardiography is widely used for assessing blood flow inside the heart and blood vessels. Currently, frame acquisition time for this method varies from tens to hundreds of milliseconds, depending on Doppler sector parameters. This leads to low frame rates of resulting video sequences equal to tens of Hz, which is insufficient for some diagnostic purposes, especially in pediatrics. In this paper, we present a new approach for reconstruction of 2D color Doppler cardiac images, which results in the frame rate being increased to hundreds of Hz. This approach relies on a modified method of frame reordering originally applied to real-time 3D echocardiography. There are no previous publications describing application of this method to 2D Color Doppler data. The approach has been tested on several in-vivo cardiac 2D color Doppler datasets with approximate duration of 30 sec and native frame rate of 15 Hz. The resulting image sequences had equivalent frame rates to 500Hz.

Measurement of the shape and motion of the mitral valve annulus has proven useful in a number of applications, including pathology diagnosis and mitral valve modeling. Current methods to delineate the annulus from four-dimensional (4D) ultrasound, however, either require extensive overhead or user-interaction, become inaccurate as they accumulate tracking error, or they do not account for annular shape or motion. This paper presents a new 4D annulus segmentation method to account for these deficiencies. The method builds on a previously published three-dimensional (3D) annulus segmentation algorithm that accurately and robustly segments the mitral annulus in a frame with a closed valve. In the 4D method, a valve state predictor determines when the valve is closed. Subsequently, the 3D annulus segmentation algorithm finds the annulus in those frames. For frames with an open valve, a constrained optical flow algorithm is used to the track the annulus. The only inputs to the algorithm are the selection of one frame with a closed valve and one user-specified point near the valve, neither of which needs to be precise. The accuracy of the tracking method is shown by comparing the tracking results to manual segmentations made by a group of experts, where an average RMS difference of 1.67 ± 0.63 mm was found across 30 tracked frames.

We describe a method to increase the frame rate for 3-dimensional ultrasound sequences of periodically moving cardiac structures by reordering the acquired volume series. The frame rate is especially important in studying intracardiac structures such as valve leaflet motion in which valve closing times are on the order of milliseconds. Current commercially available systems for volumetric ultrasound imaging are limited to approximately 10 to 20 volumes per second. Although this frame rate is sufficient for real-time observation of basic cardiac morphology, understanding cardiac dynamics requires faster frame rates. The presented work achieves higher frame rates by sampling over several beats and using a simultaneous electrocardiography signal to accurately place the frame within the cardiac cycle. The proposed method relies on periodicity of the heart motion and that within the temporal regions of highest velocity, the structural motions of interest have the lowest beat-to-beat variability.

Registration of three-dimensional ultrasound (3DUS) volumes is necessary in several applications, such as when stitching volumes to expand the field of view or when stabilizing a temporal sequence of volumes to cancel out motion of the probe or anatomy. Current systems that register 3DUS volumes either use external tracking systems (electromagnetic or optical), which add expense and impose limitations on acquisitions, or are image-based methods that operate offline and are incapable of providing immediate feedback to clinicians. This paper presents a real-time image-based algorithm for rigid registration of 3DUS volumes designed for acquisitions in which small probe displacements occur between frames. Described is a method for feature detection and descriptor formation that takes into account the characteristics of 3DUS imaging. Volumes are registered by determining a correspondence between these features. A global set of features is maintained and integrated into the registration, which limits the accumulation of registration error. The system operates in real-time (i.e. volumes are registered as fast or faster than they are acquired) by using an accelerated framework on a graphics processing unit. The algorithm’s parameter selection and performance is analyzed and validated in studies which use both water tank and clinical images. The resulting registration accuracy is comparable to similar feature-based registration methods, but in contrast to these methods, can register 3DUS volumes in real-time.

Segmenting the mitral valve during closure and throughout a cardiac cycle from four dimensional ultrasound (4DUS) is important for creation and validation of mechanical models and for improved visualization and understanding of mitral valve behavior. Current methods of segmenting the valve from 4DUS either require extensive user interaction and initialization, do not maintain the valve geometry across a cardiac cycle, or are incapable of producing a detailed coaptation line and surface. We present a method of segmenting the mitral valve annulus and leaflets from 4DUS such that a detailed, patient-specific annulus and leaflets are tracked throughout mitral valve closure, resulting in a detailed coaptation region. The method requires only the selection of two frames from a sequence indicating the start and end of valve closure and a single point near a closed valve. The annulus and leaflets are first found through direct segmentation in the appropriate frames and then by tracking the known geometry to the remaining frames. We compared the automatically segmented meshes to expert manual tracings for both a normal and diseased mitral valve, and found an average difference of 0.59±0.49mm, which is on the order of the spatial resolution of the ultrasound volumes (0.5–1.0mm/voxel).

OBJECTIVE:The most common reason for late surgical reintervention after repair of complete atrioventricular canal defects is the development of left atrioventricular valve regurgitation. We sought to determine the changes in left atrioventricular valve geometry after surgical repair that may predispose to regurgitation. METHODS:Atrioventricular valve measurements were obtained by 2-dimensional echocardiography at 3 different time points (preoperative, early postoperative, and midterm postoperative [6-12 months]). Left atrioventricular valve annulus area and left ventricular volume were calculated; vena contracta of the regurgitant jet orifice was measured. All measurements were normalized relative to an appropriate power of body surface area. RESULTS:From January 2000 to January 2008, 101 patients with complete atrioventricular canal repair were included. Left atrioventricular valve annulus was noted to remodel from an elliptical shape to a circular shape after surgery. Left atrioventricular valve annulus area increased early postoperatively (systole: 4.1 ± 0.2 cm(2)/m(2) vs 6.1 ± 0.3 cm(2)/m(2), P < .001; diastole: 7.2 ± 0.4 cm(2)/m(2) vs 10.0 ± 0.5 cm(2)/m(2), P < .001, pre- vs postoperative, respectively). This increase was sustained in the midterm postoperative period (systole: 6.1 ± 0.3 cm(2)/m(2), P = .85, vs diastole: 10.0 ± 0.4 cm(2)/m(2), P = .78, early vs midterm postoperative). Left ventricular volume increased in the early and midterm postoperative periods compared with preoperative (systole: 16.9 ± 1.2 mL/m(2) vs 26.2 ± 1.7 mL/m(2), P < .001; diastole: 35.0 ± 2.4 mL/m(2) vs 52.5 ± 3.2 mL/m(2), P < .001). CONCLUSIONS:Complete atrioventricular canal repair leads to left atrioventricular valve annular shape change with increased area and circular shape. The change in left atrioventricular valve annulus shape appeared to be mainly due to increased circumference in the posterior free wall of the annulus. These findings may provide a mechanism for the progression of central regurgitation seen after complete atrioventricular canal repair and a potential solution.

The shape of the mitral valve annulus is used in diagnostic and modeling applications, yet methods to accurately and reproducibly delineate the annulus are limited. This paper presents a mitral annulus segmentation algorithm designed for closed mitral valves which locates the annulus in three-dimensional ultrasound using only a single user-specified point near the center of the valve. The algorithm first constructs a surface at the location of the thin leaflets, and then locates the annulus by finding where the thin leaflet tissue meets the thicker heart wall. The algorithm iterates until convergence metrics are satisfied, resulting in an operator-independent mitral annulus segmentation. The accuracy of the algorithm was assessed from both a diagnostic and surgical standpoint by comparing the algorithm's results to delineations made by a group of experts on clinical ultrasound images of the mitral valve, and to delineations made by an expert with a surgical view of the mitral annulus on excised porcine hearts using an electromagnetically tracked pointer. In the former study, the algorithm was statistically indistinguishable from the best performing expert (p = 0.85) and had an average RMS difference of 1.81 +/- 0.78 mm to the expert average. In the latter, the average RMS difference between the algorithm's annulus and the electromagnetically tracked points across six hearts was 1.19 +/- 0.17 mm.